Lane Change Prediction for Autonomous Vehicles in Dynamic Traffic Using Machine Learning

Improving road safety and streamlining traffic represent critical goals achievable through effective lane change prediction in intricate traffic situations. To achieve this, the paper delves into forecasting lane shift decisions across diverse and challenging traffic scenarios, comparing and contrasting various machine learning methods. By harnessing visual data capturing crucial environmental parameters such as weather conditions, traffic signals, and impediments like cars and buses, the research endeavours to develop prediction algorithms capable of identifying optimal lane change maneuvers. The accuracy and dependability of five well-known machine learning algorithms, including Random Forest, Logistic Regression, Adaboost, Gradient Boosting, and Bagging Classifier, are meticulously evaluated in predicting lane change occurrences. These models undergo rigorous training and assessment using real-world traffic statistics, providing a comprehensive understanding of their behavior in various contexts. Through this comparative analysis, insights into the strengths and weaknesses of each paradigm are gleaned, offering valuable contributions to the advancement of intelligent transportation systems.